Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015 Aug 20;12(8):9920-37.
doi: 10.3390/ijerph120809920.

Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring

Affiliations

Combining HJ CCD, GF-1 WFV and MODIS Data to Generate Daily High Spatial Resolution Synthetic Data for Environmental Process Monitoring

Mingquan Wu et al. Int J Environ Res Public Health. .

Abstract

The limitations of satellite data acquisition mean that there is a lack of satellite data with high spatial and temporal resolutions for environmental process monitoring. In this study, we address this problem by applying the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Spatial and Temporal Data Fusion Approach (STDFA) to combine Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field of view camera (GF-1 WFV) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate daily high spatial resolution synthetic data for land surface process monitoring. Actual HJ CCD and GF-1 WFV data were used to evaluate the precision of the synthetic images using the correlation analysis method. Our method was tested and validated for two study areas in Xinjiang Province, China. The results show that both the ESTARFM and STDFA can be applied to combine HJ CCD and MODIS reflectance data, and GF-1 WFV and MODIS reflectance data, to generate synthetic HJ CCD data and synthetic GF-1 WFV data that closely match actual data with correlation coefficients (r) greater than 0.8989 and 0.8643, respectively. Synthetic red- and near infrared (NIR)-band data generated by ESTARFM are more suitable for the calculation of Normalized Different Vegetation Index (NDVI) than the data generated by STDFA.

Keywords: ESTARFM; GF-1 WFV; HJ CCD; STDFA.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Locations of the study areas.
Figure 2
Figure 2
Comparison of MODIS, HJ CCD and synthetic data generated by the ESTARFM and STDFA acquired on 7 October 2013: (a) MODIS data; (b) synthetic HJ CCD data generated by STDFA; (c) synthetic HJ CCD data generated by ESTARFM; (d) actual HJ CCD data.
Figure 3
Figure 3
Comparison of MODIS, GF-1 WFV and synthetic data generated by ESTARFM and STDFA acquired on 7 October 2013: (a) MODIS data; (b) synthetic GF-1 WFV data generated by STDFA; (c) synthetic GF-1 WFV data generated by ESTARFM; (d) actual GF-1 WFV data.
Figure 4
Figure 4
An example of the difference in shadow direction and length.

Similar articles

Cited by

References

    1. Politi E., Cutler M.E.J., Rowan J.S. Using the NOAA Advanced Very High Resolution Radiometer to characterise temporal and spatial trends in water temperature of large European lakes. Remote Sens. Environ. 2012;126:1–11. doi: 10.1016/j.rse.2012.08.004. - DOI
    1. Maisongrande P., Duchemin B., Dedieu G. VEGETATION/SPOT: An operational mission for the Earth monitoring; presentation of new standard products. Int. J. Remote Sens. 2004;25:9–14. doi: 10.1080/0143116031000115265. - DOI
    1. Salomonson V.V., Barnes W.L., Maymon P.W., Montgomery H.E., Ostrow H. MODIS: Advanced facility instrument for studies of the earth as a system. IEEE Trans. Geosci. Remote Sens. 1992:145–153. doi: 10.1109/36.20292. - DOI
    1. Zhou H., Aizen E., Aizen V. Deriving long term snow cover extent dataset from AVHRR and MODIS data: Central Asia case study. Remote Sens. Environ. 2013;136:146–162. doi: 10.1016/j.rse.2013.04.015. - DOI
    1. Yagoub H., Belbachir A.H., Benabadji N. Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data. Adv. Space Res. 2014;53:1686–1693. doi: 10.1016/j.asr.2014.03.020. - DOI

Publication types

LinkOut - more resources